Look at scores over time

Picking some countries that have significant changes over the past 6 years. Peru

#scores data
scores <- read_csv("../scores_2018-11-09.csv") %>%
  filter(goal == "FIS")

#region details to help us filter by country name rather than ID
rgn_deets <- read_csv("https://raw.github.com/OHI-Science/ohiprep_v2018/gh-pages/globalprep/supplementary_information/v2018/rgn_details.csv")

#pull out some specific countries

ctry_data <- scores %>%
  left_join(rgn_deets, by = c("region_id" = "rgn_id")) %>%
  filter(rgn_nam %in% c("Peru", "Ile Europa", "Amsterdam Island and Saint Paul Island", "Ile Tromelin", "Maldives", "Sierra Leone"))

Plot these countries

score <- ctry_data %>%
  filter(dimension == "score")

ggplot(score, aes(x = year, y = score, color = rgn_nam)) +
  geom_line() +
  theme_bw() +
  labs(y = "FIS score") +
  theme(legend.title=element_blank())

It’s interesting that Ile Europa, Ile Tromelin, Amsterdam Island & St. Paul Island maintain the same pattern. Maldives is slightly different but still a steep decline after 2014. Peru and Sierra Leone are strikingly different.

t <- ctry_data %>%
  filter(dimension %in% c("pressures", "status", "resilience"))

ggplot(t, aes(x = year, y = score, color = dimension)) +
  geom_line() +
  theme_bw() +
  facet_wrap(~rgn_nam) +
  theme(legend.title=element_blank())

It looks like pressures and resilience are not having a big impact on these status values. Next things to look at is catch over time and stock scores with proportional catch over time.

Looking at b/bmsy and catch data

bbmsy <- read_csv("~/github/ohi-global/eez/layers/fis_b_bmsy.csv") %>%
  filter(rgn_id %in% unique(ctry_data$region_id)) %>%
  mutate(group = 
           case_when(
             bbmsy < 0.8 ~ "overfished",
             bbmsy >= 0.8 & bbmsy <= 1.45 ~ "fully exploited",
             bbmsy > 1.45 ~ "underfished"
           ))

catch <- read_csv("~/github/ohi-global/eez/layers/fis_meancatch.csv") %>%
  filter(rgn_id %in% unique(ctry_data$region_id)) %>%
  separate(stock_id_taxonkey, into = c("stock_id", "taxonkey"), sep = "_(?=[:digit:])")

combine <- bbmsy %>% 
  full_join(catch) %>%
  group_by(rgn_id, group, year) %>%
  summarize(catch = sum(mean_catch, na.rm=T)) %>%
  left_join(rgn_deets) %>%
  select(year, rgn_nam, group, catch) %>%
  distinct() %>%
  mutate(status = ifelse(is.na(group), "no status", group)) %>%
  filter(year > 2008)


ggplot(combine, aes(year, catch)) + 
  geom_area(aes(fill = status)) +
  facet_wrap(~rgn_nam, scales = "free") +
  scale_fill_manual(breaks = c("fully exploited", "no status", "overfished", "underfished"), 
                       values=c("darkblue", "orange", "darkred", "darkgreen")) +
  theme_bw() +
  theme(axis.text.x = element_text(angle = 45, hjust = 1))

What stocks are making up Sierra Leone’s catch?

sl <- catch %>% 
  filter(rgn_id == 96,
         year > 2008) %>%
  left_join(bbmsy) %>%
  group_by(year) %>%
  mutate(catch_prop = mean_catch/sum(mean_catch))

plotly::ggplotly(ggplot(sl, aes(year, catch_prop)) + 
  geom_area(aes(fill = stock_id)) +
  theme_bw() +
  theme(legend.position=""))

It looks like Ethmalosa fimbriata - 34 makes up a significant portion of the catch! Almost 60% of catch. This is a Bonga Shad

shad_bbmsy <- bbmsy %>%
  filter(stock_id == "Ethmalosa_fimbriata-34")

ggplot(shad_bbmsy, aes(x = year, y = bbmsy)) +
  geom_line() +
  theme_bw() +
  geom_hline(yintercept = 1.45, color = "darkgreen", lty = "dashed") +
  ylab("B/Bmsy") +
  xlab("Year") +
  ggtitle("Bonga Shad FAO area 34")

This seems to be a large fishery for West Africa (FAO). And the status has been getting closer to 1 so it improves the score. The dashed green line shows where we define underexploted (>1.45) and fully exploited (<1.45) which explains the dramatic shift in the plot.

Let’s see what’s going on with Peru

peru <- catch %>% 
  filter(rgn_id == 138,
         year > 2008) %>%
  left_join(bbmsy) %>%
  group_by(year) %>%
  mutate(catch_prop = mean_catch/sum(mean_catch))

plotly::ggplotly(ggplot(peru, aes(year, catch_prop)) + 
  geom_area(aes(fill = stock_id)) +
  theme_bw() +
  theme(legend.position=""))

Again we have a single fishery making up almost 68% of the total catch. This isn’t surprising as Peruvian anchoveta is one of the largest fisheries in the world.

anch_bbmsy <- bbmsy %>%
  filter(stock_id == "Engraulis_ringens-87") %>%
  filter(year > 2007)

ggplot(anch_bbmsy, aes(x = year, y = bbmsy)) +
  geom_line() +
  theme_bw() +
  geom_hline(yintercept = 1.45, color = "darkgreen", lty = "dashed") +
  labs(y = "B/Bmsy",
       x = "Year",
       title = "Engraulis ringens FAO area 87")

Let’s look at the islands. Ile Europa and Ile Tromelin are geographically close, near Madagascar.

Iles <- catch %>% 
  filter(rgn_id %in% c(35,36),
         year > 2008) %>%
  left_join(bbmsy) %>%
  group_by(year, rgn_id) %>%
  mutate(catch_prop = mean_catch/sum(mean_catch)) %>%
  left_join(rgn_deets)

plotly::ggplotly(ggplot(Iles, aes(year, catch_prop)) + 
  geom_area(aes(fill = stock_id)) +
  theme_bw() +
  theme(legend.position="") +
  facet_wrap(~rgn_nam))

Ok it looks like Tuna is a significant portion of catch for these islands.

ile_catch <- Iles %>%
  filter(catch_prop > 0.1,
         !is.na(bbmsy))

ggplot(ile_catch, aes(x = year, y = bbmsy, color = stock_id)) +
  geom_line() +
  theme_bw() +
  labs(y = "B/Bmsy",
       x = "Year",
       ggtitle = "Tuna status for FAO 51") +
  theme(legend.title=element_blank())

Let’s look at the Maldives

maldives <- catch %>% 
  filter(rgn_id == 39,
         year > 2008) %>%
  left_join(bbmsy) %>%
  group_by(year) %>%
  mutate(catch_prop = mean_catch/sum(mean_catch)) %>%
  left_join(rgn_deets)

plotly::ggplotly(ggplot(maldives, aes(year, catch_prop)) + 
  geom_area(aes(fill = stock_id)) +
  theme_bw() +
  theme(legend.position=""))

Again Katsuwonus pelamis (Skipjack Tuna) is majority of catch.

Is Amsterdam Island also highly dependant on Skipjack? It’s much more subpolar (near Antarctic)

ams <- catch %>% 
  filter(rgn_id == 92,
         year > 2008) %>%
  left_join(bbmsy) %>%
  group_by(year) %>%
  mutate(catch_prop = mean_catch/sum(mean_catch)) %>%
  left_join(rgn_deets)

plotly::ggplotly(ggplot(ams, aes(year, catch_prop)) + 
  geom_area(aes(fill = stock_id)) +
  theme_bw() +
  theme(legend.position=""))

This time is Thunnus albacares (Yellowfin tuna) which has a similar trajectory to Skipjack.